Multitemporal SAR RGB Processing for Sentinel-1 GRD Products: Methodology and Applications

Amitrano, Donato; Guida, Raffaella; Ruello, Giuseppe

doi:10.1109/jstars.2019.2904035

Cited by 18 publications

(8 citation statements)

References 25 publications

(37 reference statements)

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“…Long-term and NRT publicly available RS datasets within GEE along with its high-performance computing promote this cloud-based platform for monitoring, forecasting, prevention, vulnerability, and resilience studies of natural disasters. In particular, GEE was utilized for drought monitoring [129], [130], flood mapping and flood risk assessment [131], [132], wildfire severity mapping [133], [134], landslides analyses [135], hurricane studies [136], and tsunami studies [137]. For instance, MODIS and meteorological datasets were employed within GEE to study the temporal and spatial variations of drought events in Potohar Plateau of Punjab, Pakistan between 2000 and 2015 [129].…”

Section: F Natural Disastermentioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

660

216

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Remote Sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest (RF), were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use (LCLU) classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications has significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.

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Section: F Natural Disastermentioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

660

216

View full text Add to dashboard Cite

show abstract

“…In this study, the methodologies used were the RGB composition and the calibrated threshold technique. RGB composition is a suitable method for visualizing multi-temporal changes and facilitates detection of changes on the ground surface through a temporal color image [31]. It is a method based on the differences between preand post-event images, and in which a combination of bands is established so that these differences become visually noticeable.…”

Section: Sar Sentinel-1 Data Processingmentioning

confidence: 99%

Use of Sentinel-1 Data in Flood Mapping in the Buna River Area

Abazaj

Hasko

2020

IJECC

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Floods are one of the disasters that cause many human lives and property. In Albania, most floods are associated with periods of heavy rainfall. In recent years, Synthetic Aperture Radar (SAR) sensors, which provide reliable data in all weather conditions and day and night, have been favored because they eliminate the limitations of optical images. In this study, a flood occurred in the Buna River region in March 2018, was mapped using SAR Sentinel-1 data. The aim of this study is to investigate the potential of flood mapping using SAR images using different methodologies. Sentinel-1A / B SAR images of the study area were obtained from the European Space Agency (ESA). Preprocessing steps, which include trajectory correction, calibration, speckle filtering, and terrain correction, have been applied to the images. RGB composition and the calibrated threshold technique have been applied to SAR images to detect flooded areas and the results are discussed here.

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“…The RGB composition is an appropriate method to visualize multi-temporal modifications and facilitates the detection of changes on the terrain surface through a temporal color image [58]. It is a method based on the differences between the pre-and post-event images, and in which a combination of bands is set for these differences to become visually remarkable.…”

Section: Sar Sentinel-1 Data Processingmentioning

confidence: 99%

Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study

Carreño

Muñoz

2019

Water

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Flooding is the most widespread hydrological hazard worldwide that affects water management, nature protection, economic activities, hydromorphological alterations on ecosystem services, and human health. The mitigation of the risks associated with flooding requires a certain management of flood zones, sustained by data and information about the events with the help of flood maps with sufficient temporal and spatial resolution. This paper presents the potential use of the Sentinel-1 SAR (Synthetic Aperture Radar) images as a powerful tool for flood mapping applied in the event of extraordinary floods that occurred during the month of April 2018 in the Ebro (Spain). More specifically, in this study, we describe accurate and robust processing that allows real-time flood extension maps to be obtained, which is essential for risk mitigation. Evaluating the different Sentinel-1 parameters, our analysis shows that the best results on the final flood mapping for this study area were obtained using VH (Vertical-Horizontal) polarization configuration and Lee filtering 7 × 7 window sizes. Two methods were applied to flood maps from Sentinel-1 SAR images: (1) RGB (Red, Green, Blue color model) composition based on the differences between the pre- and post-event images; and (2) the calibration threshold technique or binarization based on histogram backscatter values. When comparing our flood maps with the flood areas digitalized from vertical aerial photographs, done by the Hydrological Planning Office of the Ebro Hydrographic Confederation, the results were coincident. The result of the flooding map obtained with the RADAR (Radio Detection and Ranging) image were compared with the layers with different return periods (10, 50, 100, and 500 years) for a selected zone of the study area of SNCZI (National Flood Zone Mapping System in Spain). It was found that the images are consistent and correspond to a flood between 10 and 50 years of return. In view of the results obtained, the usefulness of Sentinel-1 images as baseline data for the improvement of the methodological guide is appreciated, and should be used as a new source of input, calibration, and validation for hydrological models to improve the accuracy of flood risk maps.

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Multitemporal SAR RGB Processing for Sentinel-1 GRD Products: Methodology and Applications

Cited by 18 publications

References 25 publications

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Use of Sentinel-1 Data in Flood Mapping in the Buna River Area

Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study

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